SESP-SPOTIS: Advancing Stochastic Approach for Re-identifying MCDA Models

Multi-Criteria Decision Analysis (MCDA) is an interdisciplinary field that addresses decision-making problems that involve multiple conflicting criteria. MCDA methods are widely applied in various domains, including medicine, management, energy, and logistics. Despite their widespread use, MCDA techniques continuously evolve to address emerging challenges. This paper presents a new method called Stochastic Expected Solution Point SPOTIS (SESP-SPOTIS), for re-identifying MCDA models. SESP-SPOTIS conducts a stochastic search for the Expected Solution Point (ESP) which is then utilized within the Stable Preference Ordering Towards Ideal Solution (SPOTIS) framework. The study delves into comprehensive investigations of MCDA model re-identification and examines how the updated model influences the ranking of analyzed alternatives. Furthermore, the experiments were divided into training sets and tests to evaluate the similarity of the proposed approach, using two rank correlation coefficients, namely Weighted Spearman ( \(r_w\) ) and Weighted Similarity (WS). The results demonstrate that SESP-SPOTIS effectively re-identifies updated models and provides additional information from analysis as an ESP, thereby broadening knowledge and understanding in the decision-making process of the analyzed problem. By integrating machine learning models and stochastic optimization techniques, SESP-SPOTIS contributes to advancing the methodologies for MCDA model re-identification.